Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations4805
Missing cells557
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 MiB
Average record size in memory577.4 B

Variable types

Text4
Numeric14
Categorical3

Alerts

52 Weeks High is highly overall correlated with 52 Weeks Low and 6 other fieldsHigh correlation
52 Weeks Low is highly overall correlated with 52 Weeks High and 6 other fieldsHigh correlation
Currency is highly overall correlated with EPS Annual and 1 other fieldsHigh correlation
EPS Annual is highly overall correlated with 52 Weeks High and 7 other fieldsHigh correlation
Market Cap (in M) is highly overall correlated with 52 Weeks High and 10 other fieldsHigh correlation
Performance (52 weeks) is highly overall correlated with Market Cap (in M) and 1 other fieldsHigh correlation
Price is highly overall correlated with 52 Weeks High and 7 other fieldsHigh correlation
Price 52 Weeks Ago is highly overall correlated with 52 Weeks High and 6 other fieldsHigh correlation
ROI Annual is highly overall correlated with 52 Weeks High and 6 other fieldsHigh correlation
Résultat net is highly overall correlated with 52 Weeks High and 7 other fieldsHigh correlation
Total assets is highly overall correlated with Market Cap (in M) and 2 other fieldsHigh correlation
Volume 1 month is highly overall correlated with Market Cap (in M) and 2 other fieldsHigh correlation
Volume 52 weeks is highly overall correlated with Market Cap (in M) and 2 other fieldsHigh correlation
Currency is highly imbalanced (91.1%)Imbalance
Performance (52 weeks) has 185 (3.9%) missing valuesMissing
Price 52 Weeks Ago has 161 (3.4%) missing valuesMissing
Total assets has 122 (2.5%) missing valuesMissing
Price is highly skewed (γ1 = 27.45390208)Skewed
Market Cap (in M) is highly skewed (γ1 = 25.01426029)Skewed
Volume 52 weeks is highly skewed (γ1 = 44.27546617)Skewed
Volume 1 month is highly skewed (γ1 = 27.57585096)Skewed
52 Weeks High is highly skewed (γ1 = 34.86382144)Skewed
52 Weeks Low is highly skewed (γ1 = 23.64047796)Skewed
Résultat net is highly skewed (γ1 = -69.25964521)Skewed
Price 52 Weeks Ago is highly skewed (γ1 = 34.11919126)Skewed
EPS Annual is highly skewed (γ1 = -37.60002799)Skewed
ROI Annual is highly skewed (γ1 = -52.80777134)Skewed
Ratio Debt/Equity (Annual) is highly skewed (γ1 = 35.10264842)Skewed
Symbol has unique valuesUnique
Company Name has unique valuesUnique
Market Cap (in M) has unique valuesUnique
Beta has unique valuesUnique
Ratio Debt/Equity (Annual) has 1023 (21.3%) zerosZeros

Reproduction

Analysis started2024-08-12 18:04:43.773150
Analysis finished2024-08-12 18:05:04.408310
Duration20.64 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Symbol
Text

UNIQUE 

Distinct4805
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size284.7 KiB
2024-08-12T20:05:04.663706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.637461
Min length1

Characters and Unicode

Total characters17478
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4805 ?
Unique (%)100.0%

Sample

1st rowTRNS
2nd rowACRV
3rd rowCOLM
4th rowZCMD
5th rowMOVE
ValueCountFrequency (%)
trns 1
 
< 0.1%
itri 1
 
< 0.1%
zcmd 1
 
< 0.1%
move 1
 
< 0.1%
nmih 1
 
< 0.1%
gnta 1
 
< 0.1%
allr 1
 
< 0.1%
rrbi 1
 
< 0.1%
hckt 1
 
< 0.1%
telo 1
 
< 0.1%
Other values (4795) 4795
99.8%
2024-08-12T20:05:05.073548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 1259
 
7.2%
C 1222
 
7.0%
T 1162
 
6.6%
S 1159
 
6.6%
R 1138
 
6.5%
N 1013
 
5.8%
L 904
 
5.2%
I 882
 
5.0%
E 799
 
4.6%
M 793
 
4.5%
Other values (16) 7147
40.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17478
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1259
 
7.2%
C 1222
 
7.0%
T 1162
 
6.6%
S 1159
 
6.6%
R 1138
 
6.5%
N 1013
 
5.8%
L 904
 
5.2%
I 882
 
5.0%
E 799
 
4.6%
M 793
 
4.5%
Other values (16) 7147
40.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17478
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1259
 
7.2%
C 1222
 
7.0%
T 1162
 
6.6%
S 1159
 
6.6%
R 1138
 
6.5%
N 1013
 
5.8%
L 904
 
5.2%
I 882
 
5.0%
E 799
 
4.6%
M 793
 
4.5%
Other values (16) 7147
40.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17478
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1259
 
7.2%
C 1222
 
7.0%
T 1162
 
6.6%
S 1159
 
6.6%
R 1138
 
6.5%
N 1013
 
5.8%
L 904
 
5.2%
I 882
 
5.0%
E 799
 
4.6%
M 793
 
4.5%
Other values (16) 7147
40.9%

Company Name
Text

UNIQUE 

Distinct4805
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size360.2 KiB
2024-08-12T20:05:05.298141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length55
Median length40
Mean length19.7282
Min length2

Characters and Unicode

Total characters94794
Distinct characters74
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4805 ?
Unique (%)100.0%

Sample

1st rowTranscat Inc
2nd rowAcrivon Therapeutics Inc
3rd rowColumbia Sportswear Co
4th rowZhongchao Inc
5th rowMovano Inc
ValueCountFrequency (%)
inc 3004
 
20.6%
corp 879
 
6.0%
ltd 405
 
2.8%
holdings 370
 
2.5%
group 285
 
1.9%
co 192
 
1.3%
therapeutics 186
 
1.3%
financial 123
 
0.8%
acquisition 117
 
0.8%
technologies 117
 
0.8%
Other values (5073) 8938
61.2%
2024-08-12T20:05:05.647439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9811
 
10.3%
n 8065
 
8.5%
e 6343
 
6.7%
o 6056
 
6.4%
c 5691
 
6.0%
r 5506
 
5.8%
i 5395
 
5.7%
a 5249
 
5.5%
t 4372
 
4.6%
s 3780
 
4.0%
Other values (64) 34526
36.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 94794
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9811
 
10.3%
n 8065
 
8.5%
e 6343
 
6.7%
o 6056
 
6.4%
c 5691
 
6.0%
r 5506
 
5.8%
i 5395
 
5.7%
a 5249
 
5.5%
t 4372
 
4.6%
s 3780
 
4.0%
Other values (64) 34526
36.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 94794
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9811
 
10.3%
n 8065
 
8.5%
e 6343
 
6.7%
o 6056
 
6.4%
c 5691
 
6.0%
r 5506
 
5.8%
i 5395
 
5.7%
a 5249
 
5.5%
t 4372
 
4.6%
s 3780
 
4.0%
Other values (64) 34526
36.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 94794
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9811
 
10.3%
n 8065
 
8.5%
e 6343
 
6.7%
o 6056
 
6.4%
c 5691
 
6.0%
r 5506
 
5.8%
i 5395
 
5.7%
a 5249
 
5.5%
t 4372
 
4.6%
s 3780
 
4.0%
Other values (64) 34526
36.4%

Price
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3511
Distinct (%)73.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.12111
Minimum0.0503
Maximum8506.24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.7 KiB
2024-08-12T20:05:05.766854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0503
5-th percentile0.58998
Q13.17
median12.51
Q342.99
95-th percentile193.902
Maximum8506.24
Range8506.1897
Interquartile range (IQR)39.82

Descriptive statistics

Standard deviation176.12439
Coefficient of variation (CV)3.5855132
Kurtosis1166.8143
Mean49.12111
Median Absolute Deviation (MAD)11.22
Skewness27.453902
Sum236026.93
Variance31019.801
MonotonicityNot monotonic
2024-08-12T20:05:05.873552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.16 9
 
0.2%
1.4 8
 
0.2%
1.06 8
 
0.2%
1.65 8
 
0.2%
1.04 8
 
0.2%
1.02 8
 
0.2%
1.01 8
 
0.2%
1.51 7
 
0.1%
1.1 7
 
0.1%
11.45 7
 
0.1%
Other values (3501) 4727
98.4%
ValueCountFrequency (%)
0.0503 1
< 0.1%
0.075 1
< 0.1%
0.076 1
< 0.1%
0.0766 1
< 0.1%
0.08 1
< 0.1%
0.0823 1
< 0.1%
0.092 1
< 0.1%
0.0933 1
< 0.1%
0.0945 1
< 0.1%
0.0978 1
< 0.1%
ValueCountFrequency (%)
8506.24 1
< 0.1%
3443.05 1
< 0.1%
3120.25 1
< 0.1%
1974.15 1
< 0.1%
1883.62 1
< 0.1%
1752.25 1
< 0.1%
1701.48 1
< 0.1%
1521.92 1
< 0.1%
1397.26 1
< 0.1%
1259.41 1
< 0.1%

Market Cap (in M)
Real number (ℝ)

HIGH CORRELATION  SKEWED  UNIQUE 

Distinct4805
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11870.993
Minimum0.135
Maximum3287742.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.7 KiB
2024-08-12T20:05:05.981711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.135
5-th percentile6.2142117
Q185.490223
median638.382
Q33802.5471
95-th percentile40476.965
Maximum3287742.5
Range3287742.4
Interquartile range (IQR)3717.0569

Descriptive statistics

Standard deviation93276.996
Coefficient of variation (CV)7.8575565
Kurtosis741.20747
Mean11870.993
Median Absolute Deviation (MAD)624.163
Skewness25.01426
Sum57040120
Variance8.700598 × 109
MonotonicityNot monotonic
2024-08-12T20:05:06.090348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1051.552692 1
 
< 0.1%
3770.713732 1
 
< 0.1%
7893.105333 1
 
< 0.1%
47815.4984 1
 
< 0.1%
825.177626 1
 
< 0.1%
7870.135782 1
 
< 0.1%
10427.22238 1
 
< 0.1%
22.537855 1
 
< 0.1%
565.240209 1
 
< 0.1%
57.36155484 1
 
< 0.1%
Other values (4795) 4795
99.8%
ValueCountFrequency (%)
0.135 1
< 0.1%
0.202462083 1
< 0.1%
0.290874472 1
< 0.1%
0.3662907246 1
< 0.1%
0.5117001326 1
< 0.1%
0.6849636886 1
< 0.1%
0.6859719465 1
< 0.1%
0.727851 1
< 0.1%
0.7453370722 1
< 0.1%
0.7492588667 1
< 0.1%
ValueCountFrequency (%)
3287742.486 1
< 0.1%
3017962.34 1
< 0.1%
2581647.096 1
< 0.1%
2024988.839 1
< 0.1%
1752130.051 1
< 0.1%
1309863.215 1
< 0.1%
847457.4806 1
< 0.1%
690133.0242 1
< 0.1%
638928.0644 1
< 0.1%
585534.9078 1
< 0.1%

Beta
Real number (ℝ)

UNIQUE 

Distinct4805
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0494497
Minimum-9.529384
Maximum19.570795
Zeros0
Zeros (%)0.0%
Negative517
Negative (%)10.8%
Memory size37.7 KiB
2024-08-12T20:05:06.212071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-9.529384
5-th percentile-0.30310412
Q10.41528392
median0.92064005
Q31.552923
95-th percentile2.9410558
Maximum19.570795
Range29.100179
Interquartile range (IQR)1.1376391

Descriptive statistics

Standard deviation1.1880738
Coefficient of variation (CV)1.1320922
Kurtosis24.284736
Mean1.0494497
Median Absolute Deviation (MAD)0.56131525
Skewness1.5885711
Sum5042.6057
Variance1.4115193
MonotonicityNot monotonic
2024-08-12T20:05:06.329358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9319894 1
 
< 0.1%
0.64585286 1
 
< 0.1%
-0.21069922 1
 
< 0.1%
0.886118 1
 
< 0.1%
1.289463 1
 
< 0.1%
0.93625027 1
 
< 0.1%
0.90106916 1
 
< 0.1%
0.8942454 1
 
< 0.1%
0.990637 1
 
< 0.1%
0.023227256 1
 
< 0.1%
Other values (4795) 4795
99.8%
ValueCountFrequency (%)
-9.529384 1
< 0.1%
-6.9775743 1
< 0.1%
-6.976032 1
< 0.1%
-5.8663783 1
< 0.1%
-5.5417604 1
< 0.1%
-5.3560877 1
< 0.1%
-4.9759016 1
< 0.1%
-4.6895614 1
< 0.1%
-4.5976734 1
< 0.1%
-4.5330396 1
< 0.1%
ValueCountFrequency (%)
19.570795 1
< 0.1%
15.053838 1
< 0.1%
11.313087 1
< 0.1%
10.333447 1
< 0.1%
9.652831 1
< 0.1%
9.578695 1
< 0.1%
8.625989 1
< 0.1%
8.4484005 1
< 0.1%
8.446643 1
< 0.1%
7.5607805 1
< 0.1%

Volume 52 weeks
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct4803
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1498274.1
Minimum156.57371
Maximum4.6049593 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.7 KiB
2024-08-12T20:05:06.561026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum156.57371
5-th percentile12500.644
Q1100336.11
median401838.89
Q31203606.4
95-th percentile5335564.4
Maximum4.6049593 × 108
Range4.6049577 × 108
Interquartile range (IQR)1103270.3

Descriptive statistics

Standard deviation7777385.8
Coefficient of variation (CV)5.1908966
Kurtosis2537.84
Mean1498274.1
Median Absolute Deviation (MAD)358117.46
Skewness44.275466
Sum7.1992069 × 109
Variance6.048773 × 1013
MonotonicityNot monotonic
2024-08-12T20:05:06.667524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
225453.9683 2
 
< 0.1%
6648.015873 2
 
< 0.1%
50909.83794 1
 
< 0.1%
10548.4127 1
 
< 0.1%
17894714.68 1
 
< 0.1%
198006.746 1
 
< 0.1%
246150 1
 
< 0.1%
1911584.524 1
 
< 0.1%
1397101.984 1
 
< 0.1%
371817.8571 1
 
< 0.1%
Other values (4793) 4793
99.8%
ValueCountFrequency (%)
156.5737052 1
< 0.1%
665.0873016 1
< 0.1%
777.7777778 1
< 0.1%
849.2063492 1
< 0.1%
935.059761 1
< 0.1%
1153.174603 1
< 0.1%
1188.095238 1
< 0.1%
1560.309524 1
< 0.1%
1729.063492 1
< 0.1%
1755.952381 1
< 0.1%
ValueCountFrequency (%)
460495926.5 1
< 0.1%
107475531.3 1
< 0.1%
60843084.92 1
< 0.1%
60546926.98 1
< 0.1%
56703451.59 1
< 0.1%
53449109.52 1
< 0.1%
53207215.08 1
< 0.1%
52205071.83 1
< 0.1%
49901964.06 1
< 0.1%
46498527.38 1
< 0.1%

Volume 1 month
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct4741
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1695071.6
Minimum4.5454545
Maximum3.4773365 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.7 KiB
2024-08-12T20:05:06.779422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4.5454545
5-th percentile8743.4783
Q184356.522
median398626.09
Q31263139.1
95-th percentile6650625.2
Maximum3.4773365 × 108
Range3.4773364 × 108
Interquartile range (IQR)1178782.6

Descriptive statistics

Standard deviation7113967.6
Coefficient of variation (CV)4.1968538
Kurtosis1201.7628
Mean1695071.6
Median Absolute Deviation (MAD)368900
Skewness27.575851
Sum8.1448188 × 109
Variance5.0608535 × 1013
MonotonicityNot monotonic
2024-08-12T20:05:06.903630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52026.08696 3
 
0.1%
81686.95652 3
 
0.1%
1556.521739 2
 
< 0.1%
49795.65217 2
 
< 0.1%
4956.521739 2
 
< 0.1%
261339.1304 2
 
< 0.1%
217.3913043 2
 
< 0.1%
28521.73913 2
 
< 0.1%
10786.95652 2
 
< 0.1%
22382.6087 2
 
< 0.1%
Other values (4731) 4783
99.5%
ValueCountFrequency (%)
4.545454545 1
< 0.1%
65.2173913 1
< 0.1%
73.91304348 1
< 0.1%
139.1304348 1
< 0.1%
181.8181818 1
< 0.1%
182.6086957 1
< 0.1%
213.0434783 1
< 0.1%
217.3913043 2
< 0.1%
247.9130435 1
< 0.1%
362.3043478 1
< 0.1%
ValueCountFrequency (%)
347733646.8 1
< 0.1%
110366273.9 1
< 0.1%
108802565.2 1
< 0.1%
81539721.74 1
< 0.1%
78406543.48 1
< 0.1%
63759239.13 1
< 0.1%
60902782.61 1
< 0.1%
59622121.74 1
< 0.1%
58253669.57 1
< 0.1%
53438513.04 1
< 0.1%

52 Weeks High
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3694
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.16628
Minimum0.87
Maximum14400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.7 KiB
2024-08-12T20:05:07.036740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.87
5-th percentile2.372
Q18.6615
median20.99
Q358.49
95-th percentile242.264
Maximum14400
Range14399.13
Interquartile range (IQR)49.8285

Descriptive statistics

Standard deviation281.92669
Coefficient of variation (CV)4.2608817
Kurtosis1588.3171
Mean66.16628
Median Absolute Deviation (MAD)16.145
Skewness34.863821
Sum317928.98
Variance79482.658
MonotonicityNot monotonic
2024-08-12T20:05:07.156364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.05 8
 
0.2%
14 8
 
0.2%
13 8
 
0.2%
12 7
 
0.1%
3.6 7
 
0.1%
2.1 7
 
0.1%
4 7
 
0.1%
2.08 6
 
0.1%
3.5 6
 
0.1%
2.5 6
 
0.1%
Other values (3684) 4735
98.5%
ValueCountFrequency (%)
0.87 1
< 0.1%
0.8999 2
< 0.1%
0.9039 1
< 0.1%
0.909 1
< 0.1%
0.93 1
< 0.1%
0.94 1
< 0.1%
0.98 2
< 0.1%
0.99 1
< 0.1%
1.01 1
< 0.1%
1.02 1
< 0.1%
ValueCountFrequency (%)
14400 1
< 0.1%
8700 1
< 0.1%
4144.32 1
< 0.1%
3242.54 1
< 0.1%
2173.01 1
< 0.1%
1905.09 1
< 0.1%
1899.21 1
< 0.1%
1759.76 1
< 0.1%
1670.24 1
< 0.1%
1535.86 1
< 0.1%

52 Weeks Low
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3455
Distinct (%)71.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.938927
Minimum0.0004
Maximum5210.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.7 KiB
2024-08-12T20:05:07.277533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0004
5-th percentile0.42
Q12.06
median9.96
Q331.13
95-th percentile140.576
Maximum5210.49
Range5210.4896
Interquartile range (IQR)29.07

Descriptive statistics

Standard deviation117.92687
Coefficient of variation (CV)3.3752287
Kurtosis874.98318
Mean34.938927
Median Absolute Deviation (MAD)8.895
Skewness23.640478
Sum167881.54
Variance13906.746
MonotonicityNot monotonic
2024-08-12T20:05:07.395717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.75 13
 
0.3%
1 11
 
0.2%
0.65 10
 
0.2%
0.7 9
 
0.2%
1.21 9
 
0.2%
1.1 9
 
0.2%
1.04 9
 
0.2%
1.5 9
 
0.2%
1.03 8
 
0.2%
1.42 8
 
0.2%
Other values (3445) 4710
98.0%
ValueCountFrequency (%)
0.0004 1
< 0.1%
0.0131 1
< 0.1%
0.038 1
< 0.1%
0.048 1
< 0.1%
0.056 1
< 0.1%
0.064 1
< 0.1%
0.07 1
< 0.1%
0.0712 1
< 0.1%
0.0734 1
< 0.1%
0.0767 1
< 0.1%
ValueCountFrequency (%)
5210.49 1
< 0.1%
2735.3 1
< 0.1%
2379.02 1
< 0.1%
1401.0101 1
< 0.1%
1295.65 1
< 0.1%
1274.91 1
< 0.1%
1141.04 1
< 0.1%
930.72 1
< 0.1%
860.1 1
< 0.1%
811.99 1
< 0.1%

Exchange
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size292.5 KiB
NASDAQ
3121 
NYSE
1684 

Length

Max length6
Median length6
Mean length5.2990635
Min length4

Characters and Unicode

Total characters25462
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNASDAQ
2nd rowNASDAQ
3rd rowNASDAQ
4th rowNASDAQ
5th rowNASDAQ

Common Values

ValueCountFrequency (%)
NASDAQ 3121
65.0%
NYSE 1684
35.0%

Length

2024-08-12T20:05:07.503585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T20:05:07.614899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
nasdaq 3121
65.0%
nyse 1684
35.0%

Most occurring characters

ValueCountFrequency (%)
A 6242
24.5%
N 4805
18.9%
S 4805
18.9%
D 3121
12.3%
Q 3121
12.3%
Y 1684
 
6.6%
E 1684
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25462
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 6242
24.5%
N 4805
18.9%
S 4805
18.9%
D 3121
12.3%
Q 3121
12.3%
Y 1684
 
6.6%
E 1684
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25462
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 6242
24.5%
N 4805
18.9%
S 4805
18.9%
D 3121
12.3%
Q 3121
12.3%
Y 1684
 
6.6%
E 1684
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25462
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 6242
24.5%
N 4805
18.9%
S 4805
18.9%
D 3121
12.3%
Q 3121
12.3%
Y 1684
 
6.6%
E 1684
 
6.6%

Performance (52 weeks)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4612
Distinct (%)99.8%
Missing185
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean-0.014001744
Minimum-0.99985424
Maximum33.280986
Zeros0
Zeros (%)0.0%
Negative2412
Negative (%)50.2%
Memory size37.7 KiB
2024-08-12T20:05:07.712267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.99985424
5-th percentile-0.86474502
Q1-0.37402251
median-0.021960292
Q30.2191771
95-th percentile0.79509746
Maximum33.280986
Range34.28084
Interquartile range (IQR)0.59319961

Descriptive statistics

Standard deviation0.79478758
Coefficient of variation (CV)-56.763471
Kurtosis678.35888
Mean-0.014001744
Median Absolute Deviation (MAD)0.28356284
Skewness17.666075
Sum-64.688057
Variance0.6316873
MonotonicityNot monotonic
2024-08-12T20:05:07.832648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.07029265643 2
 
< 0.1%
0.387608379 2
 
< 0.1%
-0.8042474026 2
 
< 0.1%
-0.1670839982 2
 
< 0.1%
-0.06056350255 2
 
< 0.1%
-0.47592853 2
 
< 0.1%
-0.5009512193 2
 
< 0.1%
-0.7109845435 2
 
< 0.1%
-0.02673067647 1
 
< 0.1%
0.02221718028 1
 
< 0.1%
Other values (4602) 4602
95.8%
(Missing) 185
 
3.9%
ValueCountFrequency (%)
-0.9998542374 1
< 0.1%
-0.9994216567 1
< 0.1%
-0.9992965079 1
< 0.1%
-0.9982727312 1
< 0.1%
-0.9979352051 1
< 0.1%
-0.9973795645 1
< 0.1%
-0.9973444564 1
< 0.1%
-0.9966251513 1
< 0.1%
-0.9965653988 1
< 0.1%
-0.9964575223 1
< 0.1%
ValueCountFrequency (%)
33.28098556 1
< 0.1%
8.725687571 1
< 0.1%
7.564530876 1
< 0.1%
7.492914449 1
< 0.1%
6.921500924 1
< 0.1%
5.897939474 1
< 0.1%
5.736607268 1
< 0.1%
5.64065769 1
< 0.1%
5.450054284 1
< 0.1%
5.299123385 1
< 0.1%
Distinct52
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size277.0 KiB
2024-08-12T20:05:07.972454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters9610
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)0.2%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowCN
5th rowUS
ValueCountFrequency (%)
us 4012
83.5%
cn 204
 
4.2%
il 86
 
1.8%
gb 65
 
1.4%
ca 57
 
1.2%
hk 51
 
1.1%
sg 46
 
1.0%
bm 32
 
0.7%
ie 31
 
0.6%
ky 25
 
0.5%
Other values (42) 196
 
4.1%
2024-08-12T20:05:08.192325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 4068
42.3%
U 4042
42.1%
C 290
 
3.0%
N 220
 
2.3%
G 130
 
1.4%
I 128
 
1.3%
L 114
 
1.2%
B 110
 
1.1%
K 85
 
0.9%
A 77
 
0.8%
Other values (15) 346
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9610
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 4068
42.3%
U 4042
42.1%
C 290
 
3.0%
N 220
 
2.3%
G 130
 
1.4%
I 128
 
1.3%
L 114
 
1.2%
B 110
 
1.1%
K 85
 
0.9%
A 77
 
0.8%
Other values (15) 346
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9610
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 4068
42.3%
U 4042
42.1%
C 290
 
3.0%
N 220
 
2.3%
G 130
 
1.4%
I 128
 
1.3%
L 114
 
1.2%
B 110
 
1.1%
K 85
 
0.9%
A 77
 
0.8%
Other values (15) 346
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9610
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 4068
42.3%
U 4042
42.1%
C 290
 
3.0%
N 220
 
2.3%
G 130
 
1.4%
I 128
 
1.3%
L 114
 
1.2%
B 110
 
1.1%
K 85
 
0.9%
A 77
 
0.8%
Other values (15) 346
 
3.6%

Résultat net
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct4761
Distinct (%)99.2%
Missing4
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean-1.1420088 × 1010
Minimum-5.7899787 × 1013
Maximum9.3358398 × 1011
Zeros0
Zeros (%)0.0%
Negative2283
Negative (%)47.5%
Memory size37.7 KiB
2024-08-12T20:05:08.314213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-5.7899787 × 1013
5-th percentile-2.94 × 108
Q1-36380000
median1855000
Q31.30231 × 108
95-th percentile1.885572 × 109
Maximum9.3358398 × 1011
Range5.8833371 × 1013
Interquartile range (IQR)1.66611 × 108

Descriptive statistics

Standard deviation8.3575146 × 1011
Coefficient of variation (CV)-73.182573
Kurtosis4798.292
Mean-1.1420088 × 1010
Median Absolute Deviation (MAD)64891000
Skewness-69.259645
Sum-5.4827845 × 1013
Variance6.984805 × 1023
MonotonicityNot monotonic
2024-08-12T20:05:08.437897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
714000000 3
 
0.1%
1052000000 3
 
0.1%
75000000 2
 
< 0.1%
-35390000 2
 
< 0.1%
-7000000 2
 
< 0.1%
4487000064 2
 
< 0.1%
-1122000 2
 
< 0.1%
1560000000 2
 
< 0.1%
576499968 2
 
< 0.1%
206000000 2
 
< 0.1%
Other values (4751) 4779
99.5%
(Missing) 4
 
0.1%
ValueCountFrequency (%)
-5.789978722 × 10131
< 0.1%
-2.160125542 × 10101
< 0.1%
-1.176899994 × 10101
< 0.1%
-9406706688 1
< 0.1%
-7966970880 1
< 0.1%
-6808999936 1
< 0.1%
-6541000192 1
< 0.1%
-5866999808 1
< 0.1%
-5810999808 1
< 0.1%
-5791000064 1
< 0.1%
ValueCountFrequency (%)
9.335839785 × 10111
< 0.1%
1.224190525 × 10111
< 0.1%
1.019560018 × 10111
< 0.1%
8.813599949 × 10101
< 0.1%
8.765699686 × 10101
< 0.1%
7.992333926 × 10101
< 0.1%
7.974100173 × 10101
< 0.1%
5.221699994 × 10101
< 0.1%
5.143400038 × 10101
< 0.1%
4.441899827 × 10101
< 0.1%

Sector
Categorical

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size329.6 KiB
Healthcare
1052 
Financial Services
829 
Technology
711 
Industrials
574 
Consumer Cyclical
536 
Other values (6)
1103 

Length

Max length22
Median length18
Mean length13.210198
Min length6

Characters and Unicode

Total characters63475
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndustrials
2nd rowHealthcare
3rd rowConsumer Cyclical
4th rowHealthcare
5th rowHealthcare

Common Values

ValueCountFrequency (%)
Healthcare 1052
21.9%
Financial Services 829
17.3%
Technology 711
14.8%
Industrials 574
11.9%
Consumer Cyclical 536
11.2%
Real Estate 244
 
5.1%
Consumer Defensive 215
 
4.5%
Communication Services 210
 
4.4%
Energy 183
 
3.8%
Basic Materials 161
 
3.4%

Length

2024-08-12T20:05:08.667914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
healthcare 1052
15.0%
services 1039
14.8%
financial 829
11.8%
consumer 751
10.7%
technology 711
10.2%
industrials 574
8.2%
cyclical 536
7.7%
real 244
 
3.5%
estate 244
 
3.5%
defensive 215
 
3.1%
Other values (5) 805
11.5%

Most occurring characters

ValueCountFrequency (%)
e 7211
 
11.4%
a 6053
 
9.5%
c 5074
 
8.0%
i 5034
 
7.9%
l 4733
 
7.5%
n 4512
 
7.1%
s 3809
 
6.0%
r 3760
 
5.9%
t 2665
 
4.2%
o 2593
 
4.1%
Other values (21) 18031
28.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63475
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 7211
 
11.4%
a 6053
 
9.5%
c 5074
 
8.0%
i 5034
 
7.9%
l 4733
 
7.5%
n 4512
 
7.1%
s 3809
 
6.0%
r 3760
 
5.9%
t 2665
 
4.2%
o 2593
 
4.1%
Other values (21) 18031
28.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63475
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 7211
 
11.4%
a 6053
 
9.5%
c 5074
 
8.0%
i 5034
 
7.9%
l 4733
 
7.5%
n 4512
 
7.1%
s 3809
 
6.0%
r 3760
 
5.9%
t 2665
 
4.2%
o 2593
 
4.1%
Other values (21) 18031
28.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63475
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 7211
 
11.4%
a 6053
 
9.5%
c 5074
 
8.0%
i 5034
 
7.9%
l 4733
 
7.5%
n 4512
 
7.1%
s 3809
 
6.0%
r 3760
 
5.9%
t 2665
 
4.2%
o 2593
 
4.1%
Other values (21) 18031
28.4%
Distinct144
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size358.9 KiB
2024-08-12T20:05:08.862439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length40
Median length32
Mean length19.469095
Min length4

Characters and Unicode

Total characters93549
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowIndustrial Distribution
2nd rowBiotechnology
3rd rowApparel Manufacturing
4th rowHealth Information Services
5th rowMedical Devices
ValueCountFrequency (%)
2336
 
18.8%
biotechnology 602
 
4.8%
services 466
 
3.7%
software 366
 
2.9%
banks 326
 
2.6%
regional 321
 
2.6%
specialty 305
 
2.4%
medical 241
 
1.9%
application 217
 
1.7%
equipment 200
 
1.6%
Other values (189) 7078
56.8%
2024-08-12T20:05:09.191326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 8911
 
9.5%
7653
 
8.2%
i 7011
 
7.5%
t 6307
 
6.7%
a 6163
 
6.6%
n 6088
 
6.5%
o 5518
 
5.9%
r 4782
 
5.1%
s 4624
 
4.9%
c 4357
 
4.7%
Other values (38) 32135
34.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 93549
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 8911
 
9.5%
7653
 
8.2%
i 7011
 
7.5%
t 6307
 
6.7%
a 6163
 
6.6%
n 6088
 
6.5%
o 5518
 
5.9%
r 4782
 
5.1%
s 4624
 
4.9%
c 4357
 
4.7%
Other values (38) 32135
34.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 93549
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 8911
 
9.5%
7653
 
8.2%
i 7011
 
7.5%
t 6307
 
6.7%
a 6163
 
6.6%
n 6088
 
6.5%
o 5518
 
5.9%
r 4782
 
5.1%
s 4624
 
4.9%
c 4357
 
4.7%
Other values (38) 32135
34.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 93549
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 8911
 
9.5%
7653
 
8.2%
i 7011
 
7.5%
t 6307
 
6.7%
a 6163
 
6.6%
n 6088
 
6.5%
o 5518
 
5.9%
r 4782
 
5.1%
s 4624
 
4.9%
c 4357
 
4.7%
Other values (38) 32135
34.4%

Price 52 Weeks Ago
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct3693
Distinct (%)79.5%
Missing161
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean49.951347
Minimum0.30000001
Maximum11250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.7 KiB
2024-08-12T20:05:09.313630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.30000001
5-th percentile1.4400001
Q15.9487499
median15.135293
Q344.238011
95-th percentile178.88187
Maximum11250
Range11249.7
Interquartile range (IQR)38.289261

Descriptive statistics

Standard deviation222.40588
Coefficient of variation (CV)4.4524501
Kurtosis1532.3102
Mean49.951347
Median Absolute Deviation (MAD)12.180719
Skewness34.119191
Sum231974.06
Variance49464.377
MonotonicityNot monotonic
2024-08-12T20:05:09.425608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.220000029 8
 
0.2%
2.049999952 8
 
0.2%
10.69999981 8
 
0.2%
1.679999948 8
 
0.2%
3.019999981 7
 
0.1%
12 7
 
0.1%
4.199999809 7
 
0.1%
1.600000024 6
 
0.1%
10.64000034 6
 
0.1%
2.559999943 6
 
0.1%
Other values (3683) 4573
95.2%
(Missing) 161
 
3.4%
ValueCountFrequency (%)
0.3000000119 1
< 0.1%
0.400000006 1
< 0.1%
0.4300000072 1
< 0.1%
0.4379999936 1
< 0.1%
0.4799999893 1
< 0.1%
0.5009999871 1
< 0.1%
0.5099999905 1
< 0.1%
0.5170000196 1
< 0.1%
0.5210062861 1
< 0.1%
0.5320000052 1
< 0.1%
ValueCountFrequency (%)
11250 1
< 0.1%
6156.72998 1
< 0.1%
3456 1
< 0.1%
3190.70166 1
< 0.1%
2483.830078 1
< 0.1%
1566.987183 1
< 0.1%
1506.199951 1
< 0.1%
1464.240601 1
< 0.1%
1330 1
< 0.1%
1239.800049 1
< 0.1%

Currency
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct22
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size281.7 KiB
USD
4584 
CNY
 
109
EUR
 
33
BRL
 
12
CAD
 
11
Other values (17)
 
56

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters14415
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.2%

Sample

1st rowUSD
2nd rowUSD
3rd rowUSD
4th rowUSD
5th rowUSD

Common Values

ValueCountFrequency (%)
USD 4584
95.4%
CNY 109
 
2.3%
EUR 33
 
0.7%
BRL 12
 
0.2%
CAD 11
 
0.2%
HKD 10
 
0.2%
GBP 9
 
0.2%
SGD 6
 
0.1%
AUD 6
 
0.1%
JPY 5
 
0.1%
Other values (12) 20
 
0.4%

Length

2024-08-12T20:05:09.524704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
usd 4584
95.4%
cny 109
 
2.3%
eur 33
 
0.7%
brl 12
 
0.2%
cad 11
 
0.2%
hkd 10
 
0.2%
gbp 9
 
0.2%
sgd 6
 
0.1%
aud 6
 
0.1%
jpy 5
 
0.1%
Other values (12) 20
 
0.4%

Most occurring characters

ValueCountFrequency (%)
U 4623
32.1%
D 4619
32.0%
S 4591
31.8%
C 124
 
0.9%
N 117
 
0.8%
Y 117
 
0.8%
R 53
 
0.4%
E 36
 
0.2%
B 21
 
0.1%
A 18
 
0.1%
Other values (14) 96
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14415
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 4623
32.1%
D 4619
32.0%
S 4591
31.8%
C 124
 
0.9%
N 117
 
0.8%
Y 117
 
0.8%
R 53
 
0.4%
E 36
 
0.2%
B 21
 
0.1%
A 18
 
0.1%
Other values (14) 96
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14415
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 4623
32.1%
D 4619
32.0%
S 4591
31.8%
C 124
 
0.9%
N 117
 
0.8%
Y 117
 
0.8%
R 53
 
0.4%
E 36
 
0.2%
B 21
 
0.1%
A 18
 
0.1%
Other values (14) 96
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14415
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 4623
32.1%
D 4619
32.0%
S 4591
31.8%
C 124
 
0.9%
N 117
 
0.8%
Y 117
 
0.8%
R 53
 
0.4%
E 36
 
0.2%
B 21
 
0.1%
A 18
 
0.1%
Other values (14) 96
 
0.7%

Total assets
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4671
Distinct (%)99.7%
Missing122
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean1.6596032 × 108
Minimum1024
Maximum1.52041 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.7 KiB
2024-08-12T20:05:09.625977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1024
5-th percentile3502185
Q117443900
median49557400
Q31.287695 × 108
95-th percentile5.9996702 × 108
Maximum1.52041 × 1010
Range1.5204099 × 1010
Interquartile range (IQR)1.113256 × 108

Descriptive statistics

Standard deviation5.4313698 × 108
Coefficient of variation (CV)3.2726919
Kurtosis221.45285
Mean1.6596032 × 108
Median Absolute Deviation (MAD)39449200
Skewness12.271365
Sum7.7719216 × 1011
Variance2.9499778 × 1017
MonotonicityNot monotonic
2024-08-12T20:05:09.751147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30000000 2
 
< 0.1%
46608800 2
 
< 0.1%
144976992 2
 
< 0.1%
21921800 2
 
< 0.1%
14000000 2
 
< 0.1%
132670000 2
 
< 0.1%
57145700 2
 
< 0.1%
37457200 2
 
< 0.1%
69067000 2
 
< 0.1%
116454000 2
 
< 0.1%
Other values (4661) 4663
97.0%
(Missing) 122
 
2.5%
ValueCountFrequency (%)
1024 1
< 0.1%
12443 1
< 0.1%
113809 1
< 0.1%
164495 1
< 0.1%
228025 1
< 0.1%
333008 1
< 0.1%
360600 1
< 0.1%
376141 1
< 0.1%
393449 1
< 0.1%
396368 1
< 0.1%
ValueCountFrequency (%)
1.52041001 × 10101
< 0.1%
1.049559962 × 10101
< 0.1%
8910760000 1
< 0.1%
8043539968 1
< 0.1%
7759580160 1
< 0.1%
7433039872 1
< 0.1%
7170240000 1
< 0.1%
6478000000 1
< 0.1%
6365200000 1
< 0.1%
5858999808 1
< 0.1%

EPS Annual
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct4699
Distinct (%)97.9%
Missing5
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean-11.145506
Minimum-27482.559
Maximum11880.286
Zeros4
Zeros (%)0.1%
Negative2226
Negative (%)46.3%
Memory size37.7 KiB
2024-08-12T20:05:09.874733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-27482.559
5-th percentile-9.43599
Q1-1.126725
median0.1243
Q32.19525
95-th percentile9.336315
Maximum11880.286
Range39362.844
Interquartile range (IQR)3.321975

Descriptive statistics

Standard deviation574.53815
Coefficient of variation (CV)-51.54886
Kurtosis1840.7245
Mean-11.145506
Median Absolute Deviation (MAD)1.59755
Skewness-37.600028
Sum-53498.431
Variance330094.08
MonotonicityNot monotonic
2024-08-12T20:05:09.984048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
0.1%
0.6003 3
 
0.1%
-0.0212 3
 
0.1%
-0.0396 3
 
0.1%
0.0198 3
 
0.1%
0.3315 2
 
< 0.1%
-3.5681 2
 
< 0.1%
0.2441 2
 
< 0.1%
0.0466 2
 
< 0.1%
1.8876 2
 
< 0.1%
Other values (4689) 4774
99.4%
(Missing) 5
 
0.1%
ValueCountFrequency (%)
-27482.5587 1
< 0.1%
-24710.4779 1
< 0.1%
-5086.0393 1
< 0.1%
-2997.8 1
< 0.1%
-2181 1
< 0.1%
-1952.8796 1
< 0.1%
-1586.1632 1
< 0.1%
-1564.215 1
< 0.1%
-1018.2512 1
< 0.1%
-1008.1846 1
< 0.1%
ValueCountFrequency (%)
11880.2857 1
< 0.1%
4380.6219 1
< 0.1%
2241.184 1
< 0.1%
788.6044 1
< 0.1%
463.3511 1
< 0.1%
201.4798 1
< 0.1%
149.2047 1
< 0.1%
133.0526 1
< 0.1%
117.4103 1
< 0.1%
92.8812 1
< 0.1%

ROI Annual
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3498
Distinct (%)73.3%
Missing34
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean-67.407553
Minimum-108880
Maximum12633.08
Zeros4
Zeros (%)0.1%
Negative2174
Negative (%)45.2%
Memory size37.7 KiB
2024-08-12T20:05:10.101927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-108880
5-th percentile-165.005
Q1-26.735
median1.31
Q38.34
95-th percentile25.055
Maximum12633.08
Range121513.08
Interquartile range (IQR)35.075

Descriptive statistics

Standard deviation1765.5062
Coefficient of variation (CV)-26.191519
Kurtosis3108.431
Mean-67.407553
Median Absolute Deviation (MAD)10.76
Skewness-52.807771
Sum-321601.44
Variance3117012.2
MonotonicityNot monotonic
2024-08-12T20:05:10.218790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.15 7
 
0.1%
4.19 7
 
0.1%
4.53 7
 
0.1%
1.17 6
 
0.1%
3.7 6
 
0.1%
9.32 6
 
0.1%
3.2 6
 
0.1%
7.41 5
 
0.1%
7.06 5
 
0.1%
6.62 5
 
0.1%
Other values (3488) 4711
98.0%
(Missing) 34
 
0.7%
ValueCountFrequency (%)
-108880 1
< 0.1%
-41952.85 1
< 0.1%
-29104.88 1
< 0.1%
-9780 1
< 0.1%
-5196.477 1
< 0.1%
-5033.59 1
< 0.1%
-3715.82 1
< 0.1%
-3645.8 1
< 0.1%
-3304.87 1
< 0.1%
-3184.29 1
< 0.1%
ValueCountFrequency (%)
12633.08 1
< 0.1%
2662.67 1
< 0.1%
1875.53 1
< 0.1%
1670 1
< 0.1%
1054.47 1
< 0.1%
737.69 1
< 0.1%
643.41 1
< 0.1%
445.65 1
< 0.1%
432.25 1
< 0.1%
307.73 1
< 0.1%

Ratio Debt/Equity (Annual)
Real number (ℝ)

SKEWED  ZEROS 

Distinct3285
Distinct (%)69.0%
Missing46
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean2.0707569
Minimum0
Maximum848.9286
Zeros1023
Zeros (%)21.3%
Negative0
Negative (%)0.0%
Memory size37.7 KiB
2024-08-12T20:05:10.339369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0077
median0.3223
Q31.05765
95-th percentile6.0336
Maximum848.9286
Range848.9286
Interquartile range (IQR)1.04995

Descriptive statistics

Standard deviation16.486894
Coefficient of variation (CV)7.9617722
Kurtosis1599.2902
Mean2.0707569
Median Absolute Deviation (MAD)0.3223
Skewness35.102648
Sum9854.7319
Variance271.81769
MonotonicityNot monotonic
2024-08-12T20:05:10.452786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1023
 
21.3%
0.0002 7
 
0.1%
0.0005 6
 
0.1%
0.004 6
 
0.1%
0.0008 6
 
0.1%
0.0001 6
 
0.1%
0.0032 6
 
0.1%
0.0006 5
 
0.1%
0.0182 5
 
0.1%
0.0004 5
 
0.1%
Other values (3275) 3684
76.7%
(Missing) 46
 
1.0%
ValueCountFrequency (%)
0 1023
21.3%
0.0001 6
 
0.1%
0.0002 7
 
0.1%
0.0003 2
 
< 0.1%
0.0004 5
 
0.1%
0.0005 6
 
0.1%
0.0006 5
 
0.1%
0.0007 2
 
< 0.1%
0.0008 6
 
0.1%
0.0009 1
 
< 0.1%
ValueCountFrequency (%)
848.9286 1
< 0.1%
439.3673 1
< 0.1%
262.3333 1
< 0.1%
255.8918 1
< 0.1%
181.8148 1
< 0.1%
165.6711 1
< 0.1%
136.576 1
< 0.1%
114.4921 1
< 0.1%
102.7736 1
< 0.1%
99.25 1
< 0.1%

Interactions

2024-08-12T20:05:02.397770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:44.557462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:45.793884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:47.226921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:48.510801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:49.891145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:51.262659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:52.692633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:53.975400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:55.430139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:56.760081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:58.153340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:59.545414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:05:01.041408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:05:02.485092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:44.640323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:45.885141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:47.314584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:48.589530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:49.984801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:51.349589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:52.775494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:54.068411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:55.517659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:56.846627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:58.244964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:59.634668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:05:01.131195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:05:02.578670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:44.730704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:45.985419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:47.413311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:48.684808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:50.083365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:51.443517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:52.867860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:54.167051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:55.617579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:56.940512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:58.345428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:59.736820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:05:01.225704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:05:02.671687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:44.817139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:46.083802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:47.507752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:48.774139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:50.185388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:51.536269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:52.959296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:54.265800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:55.713406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:57.025569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:58.443965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:59.848992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:05:01.321309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:05:02.760053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:44.904324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:46.179543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:47.597103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:48.860116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:50.276723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:51.625106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:53.049590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:54.357497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:55.804596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:57.120972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:58.538619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:59.941107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:05:01.410425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:05:02.864124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:44.994980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:46.276835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:47.699817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:48.956825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:50.376756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:51.725761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:53.145640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:54.457957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:55.905226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:57.218063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:58.648416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:05:00.044051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:05:01.511481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:05:02.958612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:45.083014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:46.374656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:47.795774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:49.051738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:50.475964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:51.826398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:53.239973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:54.554149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:56.003288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:57.314057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:58.746175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:05:00.146458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:05:01.606872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:05:03.044922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:45.165580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:46.462725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:47.881324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:49.136800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:50.569702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:51.918733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:53.326251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:54.646648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:56.093717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:57.400399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:58.844373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:05:00.238286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:05:01.696292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:05:03.139033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:45.253739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:46.641881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:47.970415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:49.227588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:50.666287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:52.015326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:53.418001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:54.739974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:56.196282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:57.494439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:58.946464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:05:00.344258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:05:01.796349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:05:03.232353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:45.342648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:46.740281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:48.059679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:49.419012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:50.762795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:52.215576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:53.512529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:54.844551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:56.290424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:57.589240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:59.046880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:05:00.450029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:05:01.903012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:05:03.322489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:45.427691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:46.835943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:48.147045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:49.504696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:50.855211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:52.307682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:53.600275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:54.935915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:56.388677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:57.677739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:59.144271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:05:00.552357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:05:01.998631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:05:03.419890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:45.523473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:46.940255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:48.243174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:49.596948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:50.965834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:52.410471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:53.699065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:55.146525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:56.486503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:57.774071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:59.247629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:05:00.654017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:05:02.106980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:05:03.516279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:45.622615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:47.038509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:48.339059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:49.700109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:51.070861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:52.508114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:53.793733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:55.245441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:56.581263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:57.973439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:59.351916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:05:00.859308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:05:02.210079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:05:03.604235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:45.712310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:47.135548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:48.425420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:49.800632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:51.171470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:52.599189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:53.889469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:55.337946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:56.672655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:58.065014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:04:59.451628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:05:00.951201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:05:02.306244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-08-12T20:05:10.670122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
52 Weeks High52 Weeks LowBetaCurrencyEPS AnnualExchangeMarket Cap (in M)Performance (52 weeks)PricePrice 52 Weeks AgoROI AnnualRatio Debt/Equity (Annual)Résultat netSectorTotal assetsVolume 1 monthVolume 52 weeks
52 Weeks High1.0000.883-0.0170.0000.5430.0160.7240.3630.8930.9640.5040.1920.5350.0000.2100.2830.229
52 Weeks Low0.8831.000-0.1160.0000.6930.0190.8310.4900.9810.9090.6390.2190.6420.0000.2920.2260.160
Beta-0.017-0.1161.0000.019-0.2380.1760.042-0.068-0.080-0.060-0.259-0.005-0.2430.0880.1570.2510.271
Currency0.0000.0000.0191.0000.7080.0360.0000.0000.0000.0000.0000.0000.9980.0560.0000.0000.000
EPS Annual0.5430.693-0.2380.7081.0000.0250.5890.4010.6700.5830.8480.1650.8360.0150.2420.1130.069
Exchange0.0160.0190.1760.0360.0251.0000.0470.0420.0270.0000.0190.0000.0000.4080.0680.0000.000
Market Cap (in M)0.7240.8310.0420.0000.5890.0471.0000.5160.8470.7270.5220.3080.5440.0230.7160.5950.554
Performance (52 weeks)0.3630.490-0.0680.0000.4010.0420.5161.0000.5820.2490.3680.0940.3870.0150.2070.0780.044
Price0.8930.981-0.0800.0000.6700.0270.8470.5821.0000.8910.6130.2200.6230.0000.2990.2490.180
Price 52 Weeks Ago0.9640.909-0.0600.0000.5830.0000.7270.2490.8911.0000.5480.2140.5610.0200.2140.2690.206
ROI Annual0.5040.639-0.2590.0000.8480.0190.5220.3680.6130.5481.0000.1220.7400.0410.1850.0720.036
Ratio Debt/Equity (Annual)0.1920.219-0.0050.0000.1650.0000.3080.0940.2200.2140.1221.0000.1740.0140.2780.2520.239
Résultat net0.5350.642-0.2430.9980.8360.0000.5440.3870.6230.5610.7400.1741.0000.0000.2140.1460.109
Sector0.0000.0000.0880.0560.0150.4080.0230.0150.0000.0200.0410.0140.0001.0000.0430.0000.015
Total assets0.2100.2920.1570.0000.2420.0680.7160.2070.2990.2140.1850.2780.2140.0431.0000.7630.771
Volume 1 month0.2830.2260.2510.0000.1130.0000.5950.0780.2490.2690.0720.2520.1460.0000.7631.0000.936
Volume 52 weeks0.2290.1600.2710.0000.0690.0000.5540.0440.1800.2060.0360.2390.1090.0150.7710.9361.000

Missing values

2024-08-12T20:05:03.857048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-12T20:05:04.134561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-08-12T20:05:04.320460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

SymbolCompany NamePriceMarket Cap (in M)BetaVolume 52 weeksVolume 1 month52 Weeks High52 Weeks LowExchangePerformance (52 weeks)CountryRésultat netSectorIndustryPrice 52 Weeks AgoCurrencyTotal assetsEPS AnnualROI AnnualRatio Debt/Equity (Annual)
0TRNSTranscat Inc116.29001051.5526920.9319895.090984e+047.298288e+04147.11584.450NASDAQ0.211360US15106000.0IndustrialsIndustrial Distribution96.050003USD2.553100e+071.63405.950.0185
1ACRVAcrivon Therapeutics Inc7.1800217.9911340.7118532.340076e+057.426779e+0412.8503.190NASDAQ-0.381850US-64118000.0HealthcareBiotechnology11.600000USD1.320950e+08-2.7352-49.830.0000
2COLMColumbia Sportswear Co81.83504781.3574870.6283734.619039e+055.568954e+0587.23066.010NASDAQ0.098148US227407008.0Consumer CyclicalApparel Manufacturing74.540024USD1.250472e+094.092912.970.0000
3ZCMDZhongchao Inc1.490012.008726-1.4798833.614145e+051.164410e+0612.0001.000NASDAQ-0.873369CN-11335911.0HealthcareHealth Information Services11.700000USDNaN-4.3550-62.950.0000
4MOVEMovano Inc0.371436.5252241.2164911.513081e+052.487234e+051.4000.266NASDAQ-0.715289US-27907000.0HealthcareMedical Devices1.300000USD3.505800e+07-0.6339-837.140.0142
5NMIHNMI Holdings Inc37.24002955.0584240.6787915.305636e+055.543996e+0542.00025.640NASDAQ0.280151US348496992.0Financial ServicesInsurance - Specialty29.110001USDNaN3.841313.860.2064
6GNTAGenenta Science SPA4.563581.703059-1.1924995.514391e+032.803921e+046.1002.200NASDAQ-0.199875IT-11645455.0HealthcareBiotechnology5.700000EURNaN-0.6393-57.000.0000
7ALLRAllarity Therapeutics Inc0.14266.2043603.2902102.291709e+069.179877e+0646.6000.138NASDAQ-0.996625US-12823000.0HealthcareBiotechnology41.599998USDNaN-119.6080-285.881.9720
8RRBIRed River Bancshares Inc49.3300341.0500250.6513409.616577e+031.154975e+0458.00042.780NASDAQ-0.004805US32488000.0Financial ServicesBanks - Regional49.567513USDNaN4.856611.480.0000
9HCKTHackett Group Inc25.5550711.3564920.4041379.765614e+041.270293e+0527.68020.230NASDAQ0.090217US34749000.0TechnologyInformation Technology Services23.445827USD5.962300e+071.235727.810.3631
SymbolCompany NamePriceMarket Cap (in M)BetaVolume 52 weeksVolume 1 month52 Weeks High52 Weeks LowExchangePerformance (52 weeks)CountryRésultat netSectorIndustryPrice 52 Weeks AgoCurrencyTotal assetsEPS AnnualROI AnnualRatio Debt/Equity (Annual)
4795BOXBox Inc27.514002.3695710.4103471.836657e+061.355296e+0630.9723.5650NYSE-0.094119US1.070040e+08TechnologySoftware - Infrastructure30.360001USD144976992.00.868429.886.0758
4796NSCNorfolk Southern Corp239.6254177.2276120.8009471.256250e+061.239665e+06263.66183.0900NYSE0.137450US1.791000e+09IndustrialsRailroads210.738556USD226096000.08.03436.101.3441
4797CPACopa Holdings SA88.123673.4571441.1823493.297429e+053.127565e+05114.0078.1200NYSE-0.051162PA6.713850e+08IndustrialsAirlines92.858147USD30748900.012.779613.290.8229
4798GPORGulfport Energy Corp138.142501.3117090.9882212.022063e+052.470174e+05165.13108.8400NYSE0.212501US7.523250e+08EnergyOil & Gas E&P113.989998USD18107100.077.818051.190.3025
4799BABoeing Co167.91103460.6274121.1499397.051050e+066.611630e+06267.54159.7000NYSE-0.288335US-3.441000e+09IndustrialsAerospace & Defense235.720001USD616166976.0-3.667973.7340.8466
4800IVZInvesco Ltd16.167272.5241281.2013634.633188e+064.554791e+0618.2812.4800NYSE0.027753US-3.372000e+08Financial ServicesAsset Management15.724797USD450032000.0-0.2131-0.420.5899
4801FBPFirst BanCorp19.743285.2710250.9282111.117185e+061.213457e+0622.1212.7150NYSE0.347341PR3.108070e+08Financial ServicesBanks - Regional14.663054USD163864992.01.709418.250.1080
4802SNDASonida Senior Living Inc29.36418.1133200.8845681.800198e+042.690000e+0434.266.8900NYSE1.992714US-2.302900e+07HealthcareMedical Care Facilities9.840000USD14240900.0-3.1104-3.8064.9138
4803COHRCoherent Corp63.349656.8805423.4231482.326590e+062.209304e+0680.9128.4700NYSE0.403250US-4.145670e+08TechnologyScientific & Technical Instruments45.180000USD152460992.0-1.8859-2.240.5989
4804TWLOTwilio Inc60.419701.8364001.5282492.782786e+062.422213e+0678.1649.8561NYSE-0.024610US-5.943220e+08TechnologySoftware - Infrastructure61.930000USD160600000.0-5.5389-9.460.1034